SKILLEMALL.ai

BD phy-k8s-security-audit

Kubernetes manifest security auditor (CIS Kubernetes Benchmark). Scans all YAML/JSON manifests in your repository for privileged containers, hostNetwork/hostPID/hostIPC, dangerous hostPath mounts, missing resource limits/probes, latest image tags, RBAC over-permission (cluster-admin bindings, wildcard verbs), secrets in env vars, missing NetworkPolicy, missing seccomp/AppArmor profiles. Maps findings to CIS Benchmark controls and PSS (Pod Security Standards). Zero dependencies beyond PyYAML. Zero competitors on ClawHub.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 6 886 tokens Open the sourcegithub.com analyzed 2 d ago

Kubernetes manifest security auditor (CIS Kubernetes Benchmark).

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerKubernetesSecuritySoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
94
Quality 40%
63
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 6

✓ No critical or high findings

Medium and low: 6
  • low Risky intent intent-offensive-security SKILL.md:82
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | **Restricted** | All of Baseline + non-root, no privilege escalation, seccomp, drop ALL caps |
  • low Risky intent intent-offensive-security SKILL.md:167
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    # Privilege escalation
  • low Risky intent intent-offensive-security SKILL.md:169
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    add("C002", HIGH, "Privilege escalation allowed",
    detector
  • low Risky intent intent-offensive-security SKILL.md:359
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    f"Allows {verbs & SECRET_VERBS} on secrets — enables credential harvesting.",
    detector

A further 2 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6886 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6886 tokens
  • 100Steps. 4 steps
  • 100Consistency. Name and required fields are in place

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 525: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 4 items
  • +4Has examples (7 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.